The past years have witnessed widespread adoption of deep generative models in cyber-physical systems [1]}. This is primarily due to the rise of affordable and powerful computation in the Internet of Things (IoT) era [2]}. Recently, such neural models have become increasingly demanding in terms of their memory requirements, resulting in many challenging problems. A canonical example, that we will use throughout this paper, is anomaly detection in resource-constrained edge systems that place devices close to the end user for low latency service delivery [3]}. Such systems generate enormous amounts of multivariate time-series data in the form of system device information or the user inputs [2]}, [3]}. As applications become more demanding and privacy sensitive, edge devices are more prone to breakdowns, malicious attacks and intrusions [6]}. Thus, it has become crucial to maintain resilience in edge systems. As sending all system operation data to a cloud backend is infeasible, the challenge is to efficiently run accurate system anomaly detection at the edge [7]}.
